Project Summary Identifying the susceptibility genes and variants of neuro-psychiatric diseases will not only contribute to our understanding of these diseases, but also point to potential therapeutic targets. Genome-wide association studies (GWAS) are commonly used to study complex diseases, and have been highly successful in a range of disorder, for instance, more than 100 loci have been associated with the risk of Schizophrenia through GWAS. Nevertheless, in most cases, we do not know the biological mechanisms underlying disease associated loci, because the causal variants and genes are obscured by linkage disequilibrium (LD) and by the difficulty of interpreting functional effects of most genetic variants. The goal of this project is to develop novel statistical methods for integrative analysis of genetic data of neuropsychiatric diseases to better understand the underlying genes and biological processes. (1) We will develop a method to integrate expression QTL (eQTL) data with GWAS. Our method extends the popular Transcriptome-Wise Association Studies (TWAS). TWAS aims to discover risk genes, by effectively assessing the correlation of eQTLs of a gene with the phenotype of interest. TWAS has many advantages over standard single variant-based analysis, e.g. it reduces multiple testing burden and provides biological contexts of associations. However, current TWAS methods are susceptible to false positive findings. We will develop a rigorous statistical framework to control false discoveries by accounting for pleiotropic effects of variants. (2) Fine-mapping is the statistical approach to identifying causal variants in disease-associated loci. Current fine- mapping methods, however, are often not able to narrow down specific causal variants. Our approach is based on the observation that allelic heterogeneity (AH), i.e. many variants disrupting the same gene, is common. So we can leverage AH to identify risk genes, borrowing the statistical framework of fine-mapping. (3) Researchers have developed tools to joint analyze multiple traits to improve the power of gene discovery and to identify causal risk factors of diseases. Existing approaches, however, are often based on pair-wise analysis. We will develop a powerful statistical framework to better understand common biological processes driving genetic relationships among multiple traits. Additionally, we will develop more accurate Mendelian Randomization (MR) method to identify causal relationship among traits. (4) A key component of our effort is the development of user-friendly software that could benefit the broad psychiatric genetics community.